改进YOLOv7的自动驾驶目标检测算法OA

To address the challenges of perception and detection accuracy in complex traffic environments,an enhanced YOLOv7 model is proposed to meet the demands of multi-target detection.Through feature augmentation techniques,the original network structure is op-timized to achieve multi-scale feature fusion,significantly boosting the model's feature representation capabilities.Additionally,the intro-duction of the GE attention mechanism further enhances the extraction of multi-scale features,effectively improving target detection ac-curacy.Furthermore,the integration of CoordConv in the neck and detection head of the model significantly enhances the network's abil-ity to capture spatial information,optimizing its learning capabilities and performance.The improved YOLOv7 achieves an average preci-sion of 48.2%,an increase of 5.6 percentage points,and a recall rate improvement of 9.2%.These results demonstrate that the enhanced algorithm is capable of meeting the requirements of target detection in autonomous driving's complex environments.